Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105725
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.creator | Chen, C | en_US |
dc.creator | Wang, Z | en_US |
dc.creator | Lei, Y | en_US |
dc.creator | Li, W | en_US |
dc.date.accessioned | 2024-04-15T07:36:15Z | - |
dc.date.available | 2024-04-15T07:36:15Z | - |
dc.identifier.isbn | 978-4-87974-702-0 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105725 | - |
dc.description | 26th International Conference on Computational Linguistics, December 11-16, 2016, Osaka, Japan | en_US |
dc.language.iso | en | en_US |
dc.publisher | Association for Computational Linguistics (ACL) | en_US |
dc.rights | Copyright of each paper stays with the respective authors (or their employers). | en_US |
dc.rights | Posted with permission of the author. | en_US |
dc.rights | The following publication Chengyao Chen, Zhitao Wang, Yu Lei, and Wenjie Li. 2016. Content-based Influence Modeling for Opinion Behavior Prediction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2207–2216, Osaka, Japan. The COLING 2016 Organizing Committee is available at https://aclanthology.org/C16-1208. | en_US |
dc.title | Content-based influence modeling for opinion behavior prediction | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 2207 | en_US |
dc.identifier.epage | 2216 | en_US |
dcterms.abstract | Nowadays, social media has become a popular platform for companies to understand their customers. It provides valuable opportunities to gain new insights into how a person’s opinion about a product is influenced by his friends. Though various approaches have been proposed to study the opinion formation problem, they all formulate opinions as the derived sentiment values either discrete or continuous without considering the semantic information. In this paper, we propose a Content-based Social Influence Model to study the implicit mechanism underlying the change of opinions. We then apply the learned model to predict users’ future opinions. The advantages of the proposed model is the ability to handle the semantic information and to learn two influence components including the opinion influence of the content information and the social relation factors. In the experiments conducted on Twitter datasets, our model significantly outperforms other popular opinion formation models. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In The 26th International Conference on Computational Linguistics: Proceedings of COLING 2016: Technical Papers, p. 2207-2216 | en_US |
dcterms.issued | 2016 | - |
dc.relation.ispartofbook | The 26th International Conference on Computational Linguistics: Proceedings of COLING 2016: Technical Papers | en_US |
dc.relation.conference | International Conference on Computational Linguistics [COLING] | en_US |
dc.description.validate | 202402 bcch | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | COMP-1605 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; The Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 19995949 | - |
dc.description.oaCategory | Copyright retained by author | en_US |
Appears in Collections: | Conference Paper |
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C16-1208.pdf | 368.39 kB | Adobe PDF | View/Open |
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